Zero-Forcing-Based Downlink Virtual MIMO-NOMA Communications in IoT Networks
Zero-Forcing-Based Downlink Virtual MIMO-NOMA Communications in IoT Networks
复制标题
物联网网络中基于迫零的下行链路虚拟 MIMO — NOMA 通信
DOI：
10.1109/jiot.2019.2957209
复制
发表时间：
2020-04-01
影响因子：
10.6
通讯作者：
Tsiftsis, Theodoros A.
中科院分区：
文献类型：
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作者：
Shi, Zheng;Wang, Hong;Tsiftsis, Theodoros A.
为了支持物联网(IoT)的大规模连接并提高频谱利用率，提出了一种虚拟多输入多输出(MIMO)和非正交多址(NOMA)相结合的下行链路方案。每个集群中的所有单天线物联网设备相互协作，建立一个虚拟的MIMO实体，每个集群请求多个独立的数据流。NOMA被用来叠加所有请求的数据流，每个簇利用迫零检测来解复用输入数据流。基站只有统计信道状态信息(CSI)可用，以避免在频繁的CSI估计上浪费能量和带宽。基于Kronecker模型，对虚拟MIMO-NOMA系统的中断概率和系统性能进行了深入研究。此外，渐近结果不仅有助于探索物理洞察力，而且还有助于实现产出最大化。特别是，渐近中断表达式提供了各种系统参数的定量影响，并使得分集-多路复用权衡(DMT)的研究成为可能。此外，可以适当地选择功率分配系数和/或传输速率以实现最大吞吐量。利用Karush-Kuhn-Tucker条件，将产量最大化问题转化为闭合形式，通过交替迭代优化实现联合功率和速率的选择。此外，优化算法倾向于在不利的信道条件下将更多的功率分配给集群，而在良好的信道条件下支持具有更高传输速率的集群。
To support massive connectivity and boost spectral efficiency for Internet of Things (IoT), a downlink scheme combining virtual multiple-input-multiple-output (MIMO) and nonorthogonal multiple access (NOMA) is proposed. All the single-antenna IoT devices in each cluster cooperate with each other to establish a virtual MIMO entity, and multiple independent data streams are requested by each cluster. NOMA is employed to superimpose all the requested data streams, and each cluster leverages zero-forcing detection to demultiplex the input data streams. Only statistical channel state information (CSI) is available at the base station to avoid the waste of the energy and bandwidth on frequent CSI estimations. The outage probability and goodput of the virtual MIMO-NOMA system are thoroughly investigated by considering the Kronecker model, which embraces both the transmit and receive correlations. Furthermore, the asymptotic results facilitate not only the exploration of physical insights but also the goodput maximization. In particular, the asymptotic outage expressions provide quantitative impacts of various system parameters and enable the investigation of diversity-multiplexing tradeoff (DMT). Moreover, power allocation coefficients and/or transmission rates can be properly chosen to achieve the maximal goodput. By favor of the Karush-Kuhn-Tucker conditions, the goodput maximization problems can be solved in closed form, with which the joint power and rate selection is realized by using alternately iterating optimization. Besides, the optimization algorithms tend to allocate more power to clusters under unfavorable channel conditions and support clusters with a higher transmission rate under benign channel conditions.